Multi-Denoising based Impulse Noise Removal from Images using Robust Statistical Features and Genetic Programming

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@Article{Javed:2015:MTA,

• author = "Syed Gibran Javed and Abdul Majid and Anwar M. Mirza and Asifullah Khan",
• title = "Multi-Denoising based Impulse Noise Removal from Images using Robust Statistical Features and Genetic Programming",
• journal = "Multimedia Tools and Applications",
• year = "2016",
• volume = "75",
• number = "10",
• pages = "5887--5916",
• month = may,
• keywords = "genetic algorithms, genetic programming, Image denoising, Noise detection, Mixed impulse noise, Salt and pepper noise, Impulse burst noise, Statistical features, Robust outlyingness ratio",
• publisher = "Springer",
• ISSN = "1380-7501",
• language = "English",
• URL = "http://dx.doi.org/10.1007/s11042-015-2554-0",
• DOI = "doi:10.1007/s11042-015-2554-0",
• size = "30 pages",
• abstract = "Recently, several interesting computational intelligence based image denoising techniques have been reported for the removal of either salt and pepper or uniform impulse noise. However, to the best of our knowledge, the difficult challenge of developing a multi denoising method that can remove mixed-impulse noise, uniform impulse, salt and pepper, and impulse-burst noise, has not been reported so far. In this regard, we propose a new noise removal approach called INDE-GP for the removal of multi types of impulse noises. The proposed approach consists of two stages: noise detection stage and removal stage. At first, the impulse noise is localized by a single stage GP detector that exploits various information-rich, rank-ordered and robust statistical features for detection. Next the noise is removed only from the detected noisy pixels by single stage GP estimator. This estimator is developed by exploiting the global learning capability of GP and local statistical measures of noise-free pixels present in the neighbourhood of noisy pixels. The experimental results and comparative analysis with existing denoising techniques show that multi denoising performance of the proposed INDE-GP approach is better both quantitative and qualitative ways.",
• notes = "Affiliated with Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS)

  The on-line version of this article contains supplementary material, which is available to authorized users.",

}

Genetic Programming entries for Syed Gibran Javed Abdul Majid Anwar M Mirza Asifullah Khan

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